dc.contributor.author | Parra Delgado, Alberto | |
dc.contributor.author | Tavernini, Davide | |
dc.contributor.author | Gruber, Patrick | |
dc.contributor.author | Sorniotti, Aldo | |
dc.contributor.author | Zubizarreta Pico, Asier ![ORCID](/themes/Mirage2//images/orcid_16x16.png) | |
dc.contributor.author | Pérez Rastelli, Joshue Manuel | |
dc.date.accessioned | 2024-06-07T16:59:26Z | |
dc.date.available | 2024-06-07T16:59:26Z | |
dc.date.issued | 2021-03-30 | |
dc.identifier.citation | Vehicle System Dynamics 60(6): 2098–2123 (2022) | es_ES |
dc.identifier.issn | 0042-3114 | |
dc.identifier.issn | 1744-5159 | |
dc.identifier.uri | http://hdl.handle.net/10810/68367 | |
dc.description.abstract | [EN] Future vehicle localisation technologies enable major enhancements of vehicle dynamics control. This study proposes a novel vehicle stability control paradigm, based on pre-emptive control that considers the curvature profile of the expected path ahead in the computation of the reference direct yaw moment and braking control action. The additional information allows pre-emptive trail braking control, which slows down the vehicle if the predicted speed profile based on the current torque demand is deemed incompatible with the reference trajectory ahead. Nonlinear model predictive control is used to implement the approach, in which also the steering angle and reference yaw rate provided to the internal model are varied along the prediction horizon, to account for the expected vehicle path. Two pre-emptive stability control configurations with different levels of complexity are proposed and compared with the passive vehicle, and two state-of-the-art nonlinear model predictive stability controllers, one with and one without non-pre-emptive trail braking control. The performance is assessed along obstacle avoidance tests, simulated with a high-fidelity model of an electric vehicle with in-wheel motors. Results show that the pre-emptive controllers achieve higher maximum entry speeds – up to ∼34% and ∼60% in high and low tyre-road friction conditions – than the formulations without preview. | es_ES |
dc.description.sponsorship | This work was supported in part by the Horizon 2020 Framework Programme of the European Commission under grant agreements no. 769944 (STEVE project) and no. 824311 (ACHILES project). | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Taylor & Francis | es_ES |
dc.relation | info:eu-repo/grantAgreement/EC/H2020/769944 | es_ES |
dc.relation | info:eu-repo/grantAgreement/EC/H2020/824311 | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ | * |
dc.subject | stability control | es_ES |
dc.subject | torque vectoring | es_ES |
dc.subject | direct yaw moment control | es_ES |
dc.subject | trail braking | es_ES |
dc.subject | pre-emptive control | es_ES |
dc.subject | nonlinear model predictive control | es_ES |
dc.title | On pre-emptive vehicle stability control | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.holder | © 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License | es_ES |
dc.relation.publisherversion | https://www.tandfonline.com/doi/full/10.1080/00423114.2021.1895229 | es_ES |
dc.identifier.doi | 10.1080/00423114.2021.1895229 | |
dc.contributor.funder | European Commission | |
dc.departamentoes | Ingeniería de sistemas y automática | es_ES |
dc.departamentoeu | Sistemen ingeniaritza eta automatika | es_ES |